This paper compares two artificial intelligence methods: the Decision Tree C4.5 and Rough Set Theory on the stock market data. The Decision Tree C4.5 is reviewed with the Rough Set Theory. An enhanced window application is developed to facilitate the pre-processing filtering by introducing the feature (attribute) transformations, which allows users to input formulas and create new attributes. Also, the application produces three varieties of data set with delaying, averaging, and summation. The results prove the improvement of pre-processing by applying feature (attribute) transformations on Decision Tree C4.5. Moreover, the comparison between Decision Tree C4.5 and Rough Set Theory is based on the clarity, automation, accuracy, dimensionality, raw data, and speed, which is supported by the rules sets generated by both algorithms on three different sets of data.© (2001) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.